OpenRaiser/Intern-Atlas huggingface dataset:
Return Oriented Programming - Exploit Implementation using functions
Venue: arXiv cs.CR 2017
Year: 2017

Return Oriented programming was surfaced first a decade ago, and was built to overcome the buffer exploit defense mechanisms like ASLR, DEP (or W^ X) by method of reusing the system code in the form of gadgets which are stitched together to make a Turing complete attack. And to perform Turing complete attack would require greater efforts which are quite complex, and there is very little research available for performing a Turing complete attack. So, in this project, we are systematizing the knowledge of the existing research that can be used to perform a Turing complete ROP attack.
Detecting Small Signs from Large Images
Venue: arXiv cs.CV 2017
Year: 2017

In the past decade, Convolutional Neural Networks (CNNs) have been demonstrated successful for object detections. However, the size of network input is limited by the amount of memory available on GPUs. Moreover, performance degrades when detecting small objects. To alleviate the memory usage and improve the performance of detecting small traffic signs, we proposed an approach for detecting small traffic signs from large images under real world conditions. In particular, large images are broken into small patches as input to a Small-Object-Sensitive-CNN (SOS-CNN) modified from a Single Shot Multibox Detector (SSD) framework with a VGG-16 network as the base network to produce patch-level object detection results. Scale invariance is achieved by applying the SOS-CNN on an image pyramid. Then, image-level object detection is obtained by projecting all the patch-level detection results to the image at the original scale. Experimental results on a real-world conditioned traffic sign dataset have demonstrated the effectiveness of the proposed method in terms of detection accuracy and recall, especially for those with small sizes.
Simulation optimization: A review of algorithms and applications
Venue: arXiv cs.DS 2017
Year: 2017

Simulation Optimization (SO) refers to the optimization of an objective function subject to constraints, both of which can be evaluated through a stochastic simulation. To address specific features of a particular simulation---discrete or continuous decisions, expensive or cheap simulations, single or multiple outputs, homogeneous or heterogeneous noise---various algorithms have been proposed in the literature. As one can imagine, there exist several competing algorithms for each of these classes of problems. This document emphasizes the difficulties in simulation optimization as compared to mathematical programming, makes reference to state-of-the-art algorithms in the field, examines and contrasts the different approaches used, reviews some of the diverse applications that have been tackled by these methods, and speculates on future directions in the field.
Second-Order Moment-Closure for Tighter Epidemic Thresholds
Venue: arXiv cs.SI 2017
Year: 2017

In this paper, we study the dynamics of contagious spreading processes taking place in complex contact networks. We specifically present a lower-bound on the decay rate of the number of nodes infected by a susceptible-infected-susceptible (SIS) stochastic spreading process. A precise quantification of this decay rate is crucial for designing efficient strategies to contain epidemic outbreaks. However, existing lower-bounds on the decay rate based on first-order mean-field approximations are often accompanied by a large error resulting in inefficient containment strategies. To overcome this deficiency, we derive a lower-bound based on a second-order moment-closure of the stochastic SIS processes. The proposed second-order bound is theoretically guaranteed to be tighter than existing first-order bounds. We also present various numerical simulations to illustrate how our lower-bound drastically improves the performance of existing first-order lower-bounds in practical scenarios, resulting in more efficient strategies for epidemic containment.
Model-checking Counting Temporal Logics on Flat Structures
Venue: arXiv cs.LO 2017
Year: 2017

We study several extensions of linear-time and computation-tree temporal logics with quantifiers that allow for counting how often certain properties hold. For most of these extensions, the model-checking problem is undecidable, but we show that decidability can be recovered by considering flat Kripke structures where each state belongs to at most one simple loop. Most decision procedures are based on results on (flat) counter systems where counters are used to implement the evaluation of counting operators.
Dynamic backstepping control for pure-feedback nonlinear systems
Venue: arXiv cs.SY 2017
Year: 2017

A dynamic backstepping method is proposed to design controllers for nonlinear systems in the pure-feedback form, for which the traditional backstepping method suffers from solving the implicit nonlinear algebraic equation. The idea of this method is to augment the (virtual) controls as states during each recursive step. As new dynamics are included in the design, the resulting controller is in the dynamic feedback form. Procedure of deriving the controller is detailed, and one more Lyapunov design is executed in each step compared with the traditional backstepping method. Under appropriate assumptions, the proposed control scheme achieves the uniformly asymptotically stability. The effectiveness of this method is illustrated by the stabilization and tracking numerical examples.
Error Characterization, Mitigation, and Recovery in Flash Memory Based Solid-State Drives
Venue: arXiv cs.AR 2017
Year: 2017

NAND flash memory is ubiquitous in everyday life today because its capacity has continuously increased and cost has continuously decreased over decades. This positive growth is a result of two key trends: (1) effective process technology scaling, and (2) multi-level (e.g., MLC, TLC) cell data coding. Unfortunately, the reliability of raw data stored in flash memory has also continued to become more difficult to ensure, because these two trends lead to (1) fewer electrons in the flash memory cell (floating gate) to represent the data and (2) larger cell-to-cell interference and disturbance effects. Without mitigation, worsening reliability can reduce the lifetime of NAND flash memory. As a result, flash memory controllers in solid-state drives (SSDs) have become much more sophisticated: they incorporate many effective techniques to ensure the correct interpretation of noisy data stored in flash memory cells. In this article, we review recent advances in SSD error characterization, mitigation, and data recovery techniques for reliability and lifetime improvement. We provide rigorous experimental data from state-of-the-art MLC and TLC NAND flash devices on various types of flash memory errors, to motivate the need for such techniques. Based on the understanding developed by the experimental characterization, we describe several mitigation and recovery techniques, including (1) cell-to-cell interference mitigation, (2) optimal multi-level cell sensing, (3) error correction using state-of-the-art algorithms and methods, and (4) data recovery when error correction fails. We quantify the reliability improvement provided by each of these techniques. Looking forward, we briefly discuss how flash memory and these techniques could evolve into the future.
Sensitivity analysis for network aggregative games
Venue: arXiv cs.GT 2017
Year: 2017

We investigate the sensitivity of the Nash equilibrium of constrained network aggregative games to changes in exogenous parameters affecting the cost function of the players. This setting is motivated by two applications. The first is the analysis of interventions by a social planner with a networked objective function while the second is network routing games with atomic players and information constraints. By exploiting a primal reformulation of a sensitivity analysis result for variational inequalities, we provide a characterization of the sensitivity of the Nash equilibrium that depends on primal variables only. To derive this result we assume strong monotonicity of the mapping associated with the game. As the second main result, we derive sufficient conditions that guarantee this strong monotonicity property in network aggregative games. These two characterizations allows us to systematically study changes in the Nash equilibrium due to perturbations or parameter variations in the two applications mentioned above.
A Cryptographic Approach for Steganography
Venue: arXiv cs.CR 2017
Year: 2017

In this research work, security concepts are formalized in steganography, and the common paradigms based on information theory are replaced by another ones inspired from cryptography, more practicable are closer than what is usually done in other cryptographic domains. These preliminaries lead to a first proof of a cryptographically secure information hiding scheme.
An optimization technique on pseudorandom generators based on chaotic iterations
Venue: arXiv cs.CR 2017
Year: 2017

Internet communication systems involving cryptography and data hiding often require billions of random numbers. In addition to the speed of the algorithm, the quality of the pseudo-random number generator and the ease of its implementation are common practical aspects. In this work we will discuss how to improve the quality of random numbers independently from their generation algorithm. We propose an additional implementation technique in order to take advantage of some chaotic properties. The statistical quality of our solution stems from some well-defined discrete chaotic iterations that satisfy the reputed Devaney's definition of chaos, namely the chaotic iterations technique. Pursuing recent researches published in the previous International Conference on Evolving Internet (Internet 09, 10, and 11), three methods to build pseudorandom generators by using chaotic iterations are recalled. Using standard criteria named NIST and DieHARD (some famous batteries of tests), we will show that the proposed technique can improve the statistical properties of a large variety of defective pseudorandom generators, and that the issues raised by statistical tests decrease when the power of chaotic iterations increase.
Topometric Localization with Deep Learning
Venue: arXiv cs.CV 2017
Year: 2017

Compared to LiDAR-based localization methods, which provide high accuracy but rely on expensive sensors, visual localization approaches only require a camera and thus are more cost-effective while their accuracy and reliability typically is inferior to LiDAR-based methods. In this work, we propose a vision-based localization approach that learns from LiDAR-based localization methods by using their output as training data, thus combining a cheap, passive sensor with an accuracy that is on-par with LiDAR-based localization. The approach consists of two deep networks trained on visual odometry and topological localization, respectively, and a successive optimization to combine the predictions of these two networks. We evaluate the approach on a new challenging pedestrian-based dataset captured over the course of six months in varying weather conditions with a high degree of noise. The experiments demonstrate that the localization errors are up to 10 times smaller than with traditional vision-based localization methods.
NOMA based Random Access with Multichannel ALOHA
Venue: arXiv cs.IT 2017
Year: 2017

In nonorthogonal multiple access (NOMA), the power difference of multiple signals is exploited for multiple access and successive interference cancellation (SIC) is employed at a receiver to mitigate co-channel interference. Thus, NOMA is usually employed for coordinated transmissions and mostly applied to downlink transmissions where a base station (BS) per- forms coordination for downlink transmissions with full channel state information (CSI). In this paper, however, we show that NOMA can also be employed for non-coordinated transmissions such as random access for uplink transmissions. We apply a NOMA scheme to multichannel ALOHA and show that the throughput can be improved. In particular, the resulting scheme is suitable for random access when the number of subchannels is limited since NOMA can effectively increase the number of subchannels without any bandwidth expansion.
Recurrent Residual Learning for Action Recognition
Venue: arXiv cs.CV 2017
Year: 2017

Action recognition is a fundamental problem in computer vision with a lot of potential applications such as video surveillance, human computer interaction, and robot learning. Given pre-segmented videos, the task is to recognize actions happening within videos. Historically, hand crafted video features were used to address the task of action recognition. With the success of Deep ConvNets as an image analysis method, a lot of extensions of standard ConvNets were purposed to process variable length video data. In this work, we propose a novel recurrent ConvNet architecture called recurrent residual networks to address the task of action recognition. The approach extends ResNet, a state of the art model for image classification. While the original formulation of ResNet aims at learning spatial residuals in its layers, we extend the approach by introducing recurrent connections that allow to learn a spatio-temporal residual. In contrast to fully recurrent networks, our temporal connections only allow a limited range of preceding frames to contribute to the output for the current frame, enabling efficient training and inference as well as limiting the temporal context to a reasonable local range around each frame. On a large-scale action recognition dataset, we show that our model improves over both, the standard ResNet architecture and a ResNet extended by a fully recurrent layer.
Using ECC DRAM to Adaptively Increase Memory Capacity
Venue: arXiv cs.AR 2017
Year: 2017

Modern DRAM modules are often equipped with hardware error correction capabilities, especially for DRAM deployed in large-scale data centers, as process technology scaling has increased the susceptibility of these devices to errors. To provide fast error detection and correction, error-correcting codes (ECC) are placed on an additional DRAM chip in a DRAM module. This additional chip expands the raw capacity of a DRAM module by 12.5%, but the applications are unable to use any of this extra capacity, as it is used exclusively to provide reliability for all data. In reality, there are a number of applications that do not need such strong reliability for all their data regions (e.g., some user batch jobs executing on a public cloud), and can instead benefit from using additional DRAM capacity to store extra data. Our goal in this work is to provide the additional capacity within an ECC DRAM module to applications when they do not need the high reliability of error correction. In this paper, we propose Capacity- and Reliability-Adaptive Memory (CREAM), a hardware mechanism that adapts error correcting DRAM modules to offer multiple levels of error protection, and provides the capacity saved from using weaker protection to applications. For regions of memory that do not require strong error correction, we either provide no ECC protection or provide error detection using multibit parity. We evaluate several layouts for arranging the data within ECC DRAM in these reduced-protection modes, taking into account the various trade-offs exposed from exploiting the extra chip. Our experiments show that the increased capacity provided by CREAM improves performance by 23.0% for a memory caching workload, and by 37.3% for a commercial web search workload executing production query traces. In addition, CREAM can increase bank-level parallelism within DRAM, offering further performance improvements.
Traversing a n-cube without Balanced Hamiltonian Cycle to Generate Pseudorandom Numbers
Venue: arXiv cs.DS 2017
Year: 2017

This article presents a new class of Pseudorandom Number Generators. The generators are based on traversing a n-cube where a Balanced Hamiltonian Cycle has been removed. The construction of such generators is automatic for small number of bits, but remains an open problem when this number becomes large. A running example is used throughout the paper. Finally, first statistical experiments of these generators are presented, they show how efficient and promising the proposed approach seems.
Cross-Country Skiing Gears Classification using Deep Learning
Venue: arXiv cs.CV 2017
Year: 2017

Human Activity Recognition has witnessed a significant progress in the last decade. Although a great deal of work in this field goes in recognizing normal human activities, few studies focused on identifying motion in sports. Recognizing human movements in different sports has high impact on understanding the different styles of humans in the play and on improving their performance. As deep learning models proved to have good results in many classification problems, this paper will utilize deep learning to classify cross-country skiing movements, known as gears, collected using a 3D accelerometer. It will also provide a comparison between different deep learning models such as convolutional and recurrent neural networks versus standard multi-layer perceptron. Results show that deep learning is more effective and has the highest classification accuracy.
Contraction and Deletion Blockers for Perfect Graphs and $H$-free Graphs
Venue: arXiv cs.DS 2017
Year: 2017

We study the following problem: for given integers $d$, $k$ and graph $G$, can we reduce some fixed graph parameter $π$ of $G$ by at least $d$ via at most $k$ graph operations from some fixed set $S$? As parameters we take the chromatic number $χ$, clique number $ω$ and independence number $α$, and as operations we choose the edge contraction ec and vertex deletion vd. We determine the complexity of this problem for $S=\{\mbox{ec}\}$ and $S=\{\mbox{vd}\}$ and $π\in \{χ,ω,α\}$ for a number of subclasses of perfect graphs. We use these results to determine the complexity of the problem for $S=\{\mbox{ec}\}$ and $S=\{\mbox{vd}\}$ and $π\in \{χ,ω,α\}$ restricted to $H$-free graphs.
On Compatible Triangulations with a Minimum Number of Steiner Points
Venue: arXiv cs.CG 2017
Year: 2017

Two vertex-labelled polygons are \emph{compatible} if they have the same clockwise cyclic ordering of vertices. The definition extends to polygonal regions (polygons with holes) and to triangulations---for every face, the clockwise cyclic order of vertices on the boundary must be the same. It is known that every pair of compatible $n$-vertex polygonal regions can be extended to compatible triangulations by adding $O(n^2)$ Steiner points. Furthermore, $Ω(n^2)$ Steiner points are sometimes necessary, even for a pair of polygons. Compatible triangulations provide piecewise linear homeomorphisms and are also a crucial first step in morphing planar graph drawings, aka "2D shape animation". An intriguing open question, first posed by Aronov, Seidel, and Souvaine in 1993, is to decide if two compatible polygons have compatible triangulations with at most $k$ Steiner points. In this paper we prove the problem to be NP-hard for polygons with holes. The question remains open for simple polygons.
Classification of Medical Images and Illustrations in the Biomedical Literature Using Synergic Deep Learning
Venue: arXiv cs.CV 2017
Year: 2017

The Classification of medical images and illustrations in the literature aims to label a medical image according to the modality it was produced or label an illustration according to its production attributes. It is an essential and challenging research hotspot in the area of automated literature review, retrieval and mining. The significant intra-class variation and inter-class similarity caused by the diverse imaging modalities and various illustration types brings a great deal of difficulties to the problem. In this paper, we propose a synergic deep learning (SDL) model to address this issue. Specifically, a dual deep convolutional neural network with a synergic signal system is designed to mutually learn image representation. The synergic signal is used to verify whether the input image pair belongs to the same category and to give the corrective feedback if a synergic error exists. Our SDL model can be trained 'end to end'. In the test phase, the class label of an input can be predicted by averaging the likelihood probabilities obtained by two convolutional neural network components. Experimental results on the ImageCLEF2016 Subfigure Classification Challenge suggest that our proposed SDL model achieves the state-of-the art performance in this medical image classification problem and its accuracy is higher than that of the first place solution on the Challenge leader board so far.
Modelo de Aprendizaje Biocibernetico BLM
Venue: arXiv cs.CY 2017
Year: 2017

Education in the digital period in which we live, is reaching challenges never before seen, preceded by phenomena that involve not only traditional social units, but also new virtual communities; Innovating is difficult, it is a challenge, however, we must think of new teaching methods that impact the current generation of students, who arrive with new needs and expectations. The construction of knowledge from the subject and the virtual world that surrounds it, establishes the basis for the development of a new model of teaching, where the classroom is the particular representation of a new physical-cybernetic ecosystem composed of the three large dimensions. Which are part of this new techno-social convergence (human - information - machine); Allowing an interrelation between the student, information, machine and the teacher; Using Biocybernetic methods of influence, control and replication, by means of the massive impact vector (i); In addition, the development of new strategies assisted by cybernetics and the updating of academic content according to the new teaching environment. Hence the importance of this study, which leads us to the need for a new model of transforming academic instruction, which is not based on a conglomerate of technological tools, but establishes a new educational and transformative model, based on "Collaborative Thinking" and the ubiquity of information, thus establishing the relationship between the subject and object of study, thus allowing us to establish the new Biocybernetic educational paradigm in the digital period.
Robust Lane Tracking with Multi-mode Observation Model and Particle Filtering
Venue: arXiv cs.CV 2017
Year: 2017

Automatic lane tracking involves estimating the underlying signal from a sequence of noisy signal observations. Many models and methods have been proposed for lane tracking, and dynamic targets tracking in general. The Kalman Filter is a widely used method that works well on linear Gaussian models. But this paper shows that Kalman Filter is not suitable for lane tracking, because its Gaussian observation model cannot faithfully represent the procured observations. We propose using a Particle Filter on top of a novel multiple mode observation model. Experiments show that our method produces superior performance to a conventional Kalman Filter.